1. Field of the Invention
This invention generally relates to methods and systems for binning defects detected on a specimen. Certain embodiments relate to assigning a defect to a bin corresponding to a region of interest associated with a reference image if one or more patterned features proximate to the defect match one or more patterned features in the reference image.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Fabricating semiconductor devices such as logic and memory devices typically includes processing a specimen such as a semiconductor wafer using a number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that typically involves transferring a pattern to a resist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing, etching, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a semiconductor wafer and then separated into individual semiconductor devices.
Semiconductor device design and reticle manufacturing quality are verified by different procedures before the reticle enters a semiconductor fabrication facility to begin production of integrated circuits. The semiconductor device design is checked by software simulation to verify that all features print correctly after lithography in manufacturing. Such checking is commonly referred to as “Design Rule Checking.” The output of DRC can produce a potentially large set of critical points, sometimes referred to as “hot spots” on the reticle layout. This set can be used to direct a point-to-point inspector, such as a review scanning electron microscope (SEM), but this can be highly inefficient due to the number of critical points. The reticle is inspected at the mask shop for reticle defects and measured to ensure that the features are within specification. Marginal resolution enhancing technology (RET) designs not noted by simulation checks translate into electrical failures in wafer fabrication, affect yield, and possibly remain unnoticed until wafer fabrication is complete.
Methods have been invented to address the above-described needs. These methods are often referred to as “Process Window Qualification” Methods or “PWQ” Methods and are described in U.S. Patent Application Publication No. US2004/0091142 to Peterson et al., which is incorporated by reference as if fully set forth herein. These methods were extended to include using the background behind the defects found in PWQ to bin the defects. These methods are described in U.S. patent application Ser. No. 11/005,658 filed Dec. 7, 2004 by Wu et al., which is incorporated by reference as if fully set forth herein
Reticle, photomask, and wafer inspection using either optical or electron beam imaging are important techniques for debugging semiconductor manufacturing processes, monitoring process variations, and improving production yield in the semiconductor industry. With the ever decreasing scale of modern integrated circuits (ICs) as well as the increasing complexity of the manufacturing process, inspection becomes more and more difficult. For example, the number of defects detected during each inspection process can be substantially large, and defects can be caused by many different mechanisms with severities ranging from disastrous impacts on product yields to trivial anomalies with no effect on product quality. The capability to separate defects of interest (DOI) from defects that are considered nuisance can mean the difference between a successful inspection and a failed attempt with useless data.
Many methodologies and technologies have been developed in attempts to classify a defect detected during inspection (e.g., performed during a semiconductor manufacturing process) as either a DOI or nuisance. One typical approach is to analyze the attributes of the defect such as size and magnitude and perform classification based on these attributes (e.g., using deterministic rules). However, there are situations in which defects with the same attributes occur at many areas of the device and only impact device yield or otherwise indicate serious problems when they occur in certain determinable regions of the device. In these situations, classification methods based on defect attributes will not be able to separate DOI in those defined regions of the device from nuisance in other regions. The size, geometry, and distribution of these potential regions for DOI, as well as the accuracy of the defect locations reported by inspection, make methods such as controlling the inspection recipe by wafer location and filtering by defect location impractical as ways to eliminate nuisances from inspection results. The only currently available reliable method for separating DOI from nuisance in these situations is by manually reviewing all of the defects detected during the inspection, which is a prohibitively time consuming process.
Another approach is to examine the appearance of defects or the appearance of the surrounding area and group the defects using a statistical approach such as nearest neighbor or neural network. There are, however, a number of limitations to statistical approaches. For example, statistical approaches identify “matches” that are not exact. Even if statistical approaches are supplemented with defect attributes, different defects may be grouped together. In addition, for certain layers, the DOI are present on particular patterns of background whereas the nuisance events are located on one or more other patterns. Statistical grouping does not accurately separate such defects. In the case of PWQ, statistical methods for binning defects based on background have been shown to have value, but they may produce binning results that are impure (in the sense that bins contain defects that are different in background) and inaccurate (in the sense that bins do not include all of the defects from the same background). For instance, the use case requires matching to precise background patterns, which cannot be performed using statistical methods.
A hybrid approach has been developed that uses both deterministic and statistical methods, which is described in U.S. patent application Ser. No. 10/954,968 filed on Sep. 30, 2004 by Huet et al., which is incorporated by reference as if fully set forth herein.
Another defect binning methodology used in PWQ and in standard defect analysis is to identify defects that repeat spatially on the specimen. A “repeater” is commonly defined as a defect that occurs at one point in a reticle. The currently methodology for finding repeaters is to look for common (x, y) locations in the defect results. This repeater technique only works in die-to-die defect detection if there are multiple die on the reticle. The repetition may be at the die level, reticle level (on wafers), or at the level of repeating patterns within the die such as repeating patterns in memory and test devices. Due to uncertainty in the locations of the defects, algorithms used to identify repeating defects require a tolerance around the defect locations. If the required tolerance is too large, false matches can result. For highly defective regions, such as are seen in PWQ and focus exposure matrices, this location uncertainty can result in “false matches” in which defects are binned as repeating when they are located on different backgrounds. False matches can also occur in systems with large defect location uncertainty. Another limitation of the current algorithms is that by relying on defect location alone, they cannot identify defects that are located on the same background but not at the same position on the wafer.
Accordingly, it would be advantageous to develop methods and systems for binning defects detected on a specimen that can be used to distinguish between DOI and nuisance defects based on the regions of the device in which the defects are located. It would also the advantageous to develop methods and systems for precisely identifying repeating defects on a specimen.